Robust optimization of a dynamic Black-box system under severe uncertainty: a distribution-free framework

Adolphus Lye, Masaru Kitahara, Matteo Broggi, Edoardo Patelli

Research output: Contribution to journalArticlepeer-review

Abstract

In the real world, a significant challenge faced in designing critical systems is the lack of available data. This results in a large degree of uncertainty and the need for uncertainty quantification tools so as to make risk-informed decisions. The NASA-Langley UQ Challenge 2019 seeks to provide such setting, requiring different discipline-independent approaches to address typical tasks required for the design of critical systems. This paper addresses the NASA-Langley UQ Challenge by proposing 4 key techniques to provide the solution to the challenge: (1) a distribution-free Bayesian model updating framework for the calibration of the uncertainty model; (2) an adaptive pinching approach to analyse and rank the relative sensitivity of the epistemic parameters; (3) the probability bounds analysis to estimate failure probabilities; and (4) a Non-intrusive Stochastic Simulation approach to identify an optimal design point.
Original languageEnglish
Article number108522
Number of pages58
JournalMechanical Systems and Signal Processing
Volume167
Early online date1 Nov 2021
DOIs
Publication statusE-pub ahead of print - 1 Nov 2021

Keywords

  • model class selection
  • non-intrusive imprecise stochastic simulation
  • robust optimization
  • sensitivity analysis
  • staircase density function
  • uncertainty quantification

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